Why did the Imperial College of London use totally fake models to predict over 2.2 million American deaths?!

How One Model Simulated 2.2 Million U.S. Deaths from COVID-19

By Alan Reynolds CATO Institute

When it came to dealing with an unexpected surge in infections and deaths from SARS‐​CoV‐​2 (the virus causing COVID-19 symptoms), federal and state policymakers understandably sought guidance from competing epidemiological computer models. On March 16, a 20‐​page report from Neil Ferguson’s team at Imperial College London quickly gathered enormous attention by producing enormous death estimates. Dr. Ferguson had previously publicized almost equally sensational death estimates from mad cow disease, bird flu and swine flu.

The report, which warned that an uncontrolled spread of the disease could cause as many as 510,000 deaths in Britain, triggered a sudden shift in the government’s comparatively relaxed response to the virus. American officials said the report, which projected up to 2.2 million deaths in the United States from such a spread, also influenced the White House to strengthen its measures to isolate members of the public.

A month later that 2.2 million estimate was still being used (without revealing the source) by President Trump and Doctors Fauci and Birx to imply that up to two million lives had been saved by state lockdowns and business closings and/​or by federal travel bans.

The following summary of the Ferguson/​Imperial College report provides clues about how the model came to generate such dramatic conclusions:

In the (unlikely) absence of any control measures or spontaneous changes in individual behavior, we would expect a peak in mortality (daily deaths) to occur after approximately 3 months. In such scenarios, given an estimated R0 of 2.4, we predict 81% of the G.B. and U.S. populations would be infected over the course of the epidemic… In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in G.B. and 2.2 million in the U.S., not accounting for the potential negative effects of health systems being overwhelmed on mortality.

This worst‐​case simulation came up with 2.2 million deaths by simply assuming that 81% of the population gets infected ­–268 million people– and that 0.9% of them die. It did not assume health systems would have to be overwhelmed to result in so many deaths, though it did make that prediction.

Neither the high infection rate nor the high fatality rate holds up under scrutiny.

To project that nearly everyone becomes infected the report had to assume that each person infects 2.4 others and those people, in turn, infect 2.4 others and so on, with the result that the number infected doubles roughly every four days. This 2.4 “reproduction number” (R0) was “based on … the early growth‐​rate of the epidemic in Wuhan.” But the reproduction number always appears highest during the early phase of an epidemic (partly due to increased testing) and has now fallen to nearly zero in China.

The reproduction number is not a constant, but a variable that depends on many other things, from humidity and sunlight to human behavior.

Suppose an infected man walks into a small elevator with three other people and begins coughing. The other three get infected from droplets in the air or from virus on objects (such as elevator buttons) they touch before touching their faces. In this case, we observe an R0 of 3.0. But if the coughing man is wearing a mask then perhaps one person does not become infected by inhaling the virus, so the R0 falls to 2.0. If the other two quickly use an alcohol‐​based hand sanitizer before touching their face, or wash their hands, then nobody becomes infected and the R0 falls to zero.

The worst‐​case Imperial College estimate of 2.2 million deaths if everyone does “nothing” did not simply mean no government lockdowns, as a March 31 White House graph with two curves implied. It meant nobody avoids crowded elevators, or wears face masks, washes their hands more often, or buys gloves or hand sanitizer. Everyone does literally nothing to avoid danger.The Ferguson team knew that was unrealistic, yet their phantasmal 2.2 million estimate depended on it. As they reticently acknowledged, “it is highly likely that there would be significant spontaneous change in population behavior even in the absence of government‐​mandated interventions.” An earlier February 20 brief said, “Some social distancing is to be expected, even in the absence of formal control measures.”

The obvious reality of voluntary self‐​protective actions makes it incorrect to allude to the extreme Ferguson death estimate, consciously or not, as evidence that heavy‐​handed government interventions prevented “hundreds of thousands” of deaths. In fact, the Imperial College team did not recommend “a complete lockdown which … prevents people going to work.”

The key premise of 81% of the population being infected should have raised more alarms than it did. Even the deadly “Spanish Flu” (H1N1) pandemic of 1918–19 infected no more than 28% of the U.S. population. The next H1N1 “Swine Flu” pandemic in 2009-10, infected 20-24% of Americans.

To push the percentage infected up from 20–28% to an unprecedented 81% for COVID-19 required assuming the number of cases and/​or deaths keeps doubling every three or four days for months (deaths were predicted to peak July 20). And that means assuming the estimated reproduction number (R0) of 2.4 remains high, and people keep mingling with different groups, until nearly everyone gets infected. Long before 8 out of 10 people became infected, however, a larger and larger percentage of the population would have recovered from the disease and become immune, so a smaller and smaller share would still remain susceptible.

Little more than a month after the outbreak exploded in March, COVID-19 curves are already flattening conclusively in many different countries with quite different government mitigation policies. By April 16, it was taking 60 days for the number of deaths to double in China – not 4 days. The worldwide average was up to 11 days, including 17 days in Italy, 18 days in Taiwan, and 24 in South Korea.

In short, the Imperial College projection that 81% of the U.S. population could be infected if everyone just did literally nothing to protect themselves or others is inconsistent with rational risk avoidance, history and recent experience. Even with a much smaller percentage infected, however, deaths could still end up extremely high if nearly 1% of those infected died, as the Ferguson team assumed.

The assumed 0.9% death rate (within a range of 0.4% to 1.4%) was tweaked from a smaller estimate in a study of deaths in China by Robert Verrity and others, which found a “case fatality rate” (CFR) of 1.38% among known and tested cases. By assuming that such confirmed cases underestimated actual infections by only about half, they inferred an “infection fatality rate” (IFR) of 0.66%.

Epidemiologists have since found growing evidence that the number of undetected cases with few symptoms or none is much larger than merely doubling the small number of known and tested cases. A review of such research by the Oxford University Centre for Evidence‐​Based Medicine finds “a presumed estimate for the COVID-19 IFR somewhere between 0.1% and 0.36%.” A middling estimate of 0.22% would by itself reduce the infamous 2.2 million death estimate to half a million even if 81% were somehow infected.

Eran Bendavid and Jay Bhattacharya of the Stanford School of Medicine, with 15 others, conducted serological tests for COVID-19 antibodies from a representative sample of 3,300 people from Santa Clara County, CA. The high percentage showing proof of having been cured of undetected asymptomatic cases indicates that between 48,000 to 81,000 people in Santa Clara county had already been infected and cured by the time they were tested on April 3–4. Those numbers are 50 to 85 times larger than the number of known, confirmed cases. They correspond to “an infection fatality rate of 0.12–0.2%” – similar to the flu (which nonetheless killed a CDC‐​estimated 61,000 in the 2017/18 season by infecting millions).

The Santa Clara antibody testing strongly suggests there must be sizable islands or clusters of people elsewhere in the U.S. who now have some immunity, which would substantially reduce the future risk of community spread. This is one reason any “rebound” in the fourth quarter would likely be more easily contained, even aside from the fact that we’re all much better educated, equipped and prepared if hot spots flare up in the fall. Because a newer and better Washington University IHME model ends with August 4, its low estimate of COVID-19 deaths (under 61,000 as of April 15) misses five months of 2020 and is therefore surely too optimistic for the whole year. Yet the IHME estimates will nonetheless prove enormously closer to reality than the archaic overstuffed Imperial College prediction of 2.2 million deaths.

The trouble with being too easily led by models is we can too easily be misled by models. Epidemic models may seem entirely different from economic models or climate models, but they all make terrible forecasts if filled with wrong assumptions and parameters.